Air pollution and meteorological variables' effects on COVID-19 first and second waves in Spain.

The aim of this research is to study the influence of atmospheric pollutants and meteorological variables on the incidence rate of COVID-19 and the rate of hospital admissions due to COVID-19 during the first and second waves in nine Spanish provinces. Numerous studies analyze the effect of environmental and pollution variables separately, but few that include them in the same analysis together, and even fewer that compare their effects between the first and second waves of the virus. This study was conducted in nine of 52 Spanish provinces, using generalized linear models with Poisson link between levels of PM10, NO2 and O3 (independent variables) and maximum temperature and absolute humidity and the rates of incidence and hospital admissions of COVID-19 (dependent variables), establishing a series of significant lags. Using the estimators obtained from the significant multivariate models, the relative risks associated with these variables were calculated for increases of 10 µg/m3 for pollutants, 1 °C for temperature and 1 g/m3 for humidity. The results suggest that NO2 has a greater association than the other air pollution variables and the meteorological variables. There was a greater association with O3 in the first wave and with NO2 in the second. Pollutants showed a homogeneous distribution across the country. We conclude that, compared to other air pollutants and meteorological variables, NO2 is a protagonist that may modulate the incidence and severity of COVID-19, though preventive public health measures such as masking and hand washing are still very important. Supplementary Information: The online version contains supplementary material available at 10.1007/s13762-022-04190-z.

Do Air Pollution and Meteorological Variables Have a Bearing on COVID-19 Mortality? Benchmarking of Time Series between the First and Second Waves in Nine Spanish Provinces

Some pollutants like PM10, NO2 and O-3 are detrimental to people's health, as numerous studies have shown, and they are related to short-term and long-term mortality. A sample of 9 out of the 52 Spanish provinces was studied. Generalized linear models (GLM) with a Poisson link function were developed during the time periods corresponding to the first and second waves of the daily average values of the independent variables (PM10, NO2 and O-3, as atmospheric pollutants, and meteorological variables such as the daily maximum temperature and the absolute humidity) versus the dependent variable (COVID-19 mortality rate, or CMR) during said first and second waves. Statistically significant lags between the independent variables and the dependent variable were established. The associated relative risks were calculated from the estimators obtained in the GLMs, with increases of 10 mu g/m(3) for atmospheric pollutants, 1 degrees C for the maximum temperature and 1 g/m(3) for the absolute humidity. The results show that NO2 has a stronger relationship with the CMR than the other air pollutants. The meteorological variables examined did not show a robust relationship between both waves, which indicates that they played a minor role in the CMR. In conclusion, air pollutants such as to NO2 and PM10 had a statistically significant relationship with the CMR, although it is limited and subordinate to other factors such as the public health measures that were taken, the presence of comorbidities and the age of the patient.